ReSurvCV.default: K fold cross-validation of ReSurv model.

In ReSurv: Machine Learning Models for Predicting Claim Counts

K fold cross-validation of ReSurv model.

Description

This function computes a K fold cross-validation of a pre-specified ReSurv model for a given grid of parameters.

Usage

## Default S3 method:
ReSurvCV(
  IndividualDataPP,
  model,
  hparameters_grid,
  folds,
  random_seed,
  continuous_features_scaling_method = "minmax",
  print_every_n = 1L,
  nrounds = NULL,
  early_stopping_rounds = NULL,
  epochs = 1,
  parallel = FALSE,
  ncores = 1,
  num_workers = 0,
  verbose = FALSE,
  verbose.cv = FALSE
)

Arguments

IndividualDataPP	IndividualDataPP object to use for the ReSurv fit cross-validation.
model	character, machine learning for cross validation.
hparameters_grid	list, grid of the hyperparameters to cross-validate.
folds	integer, number of folds (i.e. K).
random_seed	integer, random seed for making the code reproducible.
continuous_features_scaling_method	character, method for scaling continuous features.
print_every_n	integer, specific to the XGB approach, see xgboost::xgb.train documentation.
nrounds	integer, specific to XGB, max number of boosting iterations.
early_stopping_rounds	integer, specific to the XGB approach, see xgboost::xgb.train documentation.
epochs	integer, specific to the NN approach, epochs to be checked.
parallel	logical, specific to the NN approach, whether to use parallel computing.
ncores	integer, specific to NN, max number of cores used.
num_workers	numeric, number of workers for the NN approach, multi-process data loading with the specified number of loader worker processes.
verbose	logical, whether messages from the machine learning models must be printed.
verbose.cv	logical, whether messages from cross-validation must be printed.

Value

Best ReSurv model fit. The output is different depending on the machine learning approach that is required for cross-validation. A list containing:

• out.cv: data.frame, total output of the cross-validation (all the input parameters combinations).

• out.cv.best.oos: data.frame, combination with the best out of sample likelihood.

For XGB the columns in out.cv and out.cv.best.oos are the hyperparameters booster, eta, max_depth, subsample, alpha, lambda, min_child_weight. They also contain the metrics train.lkh, test.lkh, and the computational time time. For NN the columns in out.cv and out.cv.best.oos are the hyperparameters num_layers, optim, activation, lr, xi, eps, tie, batch_size, early_stopping, patience, node train.lkh test.lkh. They also contain the metrics train.lkh, test.lkh, and the computational time time.

References

Munir, H., Emil, H., & Gabriele, P. (2023). A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving. arXiv preprint arXiv:2312.14549.

ReSurv documentation built on April 4, 2025, 1:39 a.m.